To learn to track, we propose to make the tracking embeddings from the same instance to be closer in the latent space, or more distant, otherwise.
Here, we adopt contrastive learning [1]}, [2]}.
Specifically, for the \(i\) -th instance \(\Omega _{i}\) , we compute the center embedding \(C_{i}\)  as the average value of all the embeddings belonging to this instance:
\(C_{i} \ = \ \frac{1}{N_{i} } {\textstyle \sum _{e\in \Omega _{i} }^{}} f_{e} \ ,\) 
